Update README.md

README.md

@@ -1,199 +1,227 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- databricks/databricks-dolly-15k
+- glaiveai/glaive-code-assistant-v3
+- glaiveai/glaive-function-calling-v2
+- gretelai/synthetic_text_to_sql
+- meta-math/MetaMathQA
+- microsoft/orca-math-word-problems-200k
+- neural-bridge/rag-dataset-12000
+- neural-bridge/rag-hallucination-dataset-1000
+- nvidia/HelpSteer
+- OpenAssistant/oasst2
+language:
+- en
+- ja
+tags:
+- mixtral
+- steerlm
+base_model: tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# KARAKURI LM 8x7B Instruct v0.1
 
+![KARAKURI LM](./thumbnail.png)
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- **Developed by:** [KARAKURI Inc.](https://about.karakuri.ai/)
+- **Model type:** Mixture of Experts (MoE)
+- **Languages**: Primarily English and Japanese
+- **License:** Apache 2.0
+- **Finetuned from model:** [tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1)
+- **Contact**: For questions and comments about the model, please email `[email protected]`
+- **Demo**: https://lm.karakuri.cc/
+
+## Usage
+
+### Prompt Template
+
+The model uses the same prompt template as [Command R+](https://huggingface.co/CohereForAI/c4ai-command-r-plus), except that it contains [attribute values](#attribute-values).
+
+#### Chat
+
+```python
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("karakuri-ai/karakuri-lm-8x7b-instruct-v0.1")
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Hello!"},
+    {"role": "assistant", "content": "Hello! How can I help you today?"},
+    {"role": "user", "content": "I'm planning a day trip to Tokyo this weekend. Can you recommend a quick sightseeing plan?"}
+]
+tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+```
+
+#### Tool Use
+
+```python
+messages = [
+    {"role": "user", "content": "I'm planning a day trip to Tokyo this weekend. Can you recommend a quick sightseeing plan?"}
+]
+tools = [
+    {
+        "name": "internet_search",
+        "description": "Returns a list of relevant document snippets for a textual query retrieved from the internet",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "Query to search the internet with"
+                }
+            },
+            "required": ["query"]
+        }
+    },
+    {
+        "name": "directly_answer",
+        "description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history",
+        "parameters": {
+            "type": "object",
+            "properties": {}
+        }
+    }
+]
+tokenizer.apply_chat_template(
+    messages,
+    chat_template="tool_use",
+    tools=tools,
+    add_generation_prompt=True,
+    tokenize=False,
+)
+```
+
+#### RAG
+
+```python
+messages = [
+    {"role": "user", "content": "I'm planning a day trip to Tokyo this weekend. Can you recommend a quick sightseeing plan?"}
+]
+documents = [
+    {
+        "title": "Tsukiji Outer Market",
+        "text": "While the inner wholesale market has moved to Toyosu, Tsukiji Outer Market remains a bustling hub for fresh seafood and street food. Enjoy sushi, sashimi, and other delicacies while exploring the vibrant market streets.",
+    },
+    {
+        "title": "Meiji Shrine",
+        "text": "Nestled in a lush forest in the heart of the city, Meiji Shrine offers a peaceful retreat from the urban hustle. Dedicated to Emperor Meiji and Empress Shoken, the shrine is a popular site for traditional Japanese weddings. Stroll along the serene paths and experience a moment of tranquility."
+    }
+]
+tokenizer.apply_chat_template(
+    messages,
+    chat_template="rag",
+    documents=documents,
+    add_generation_prompt=True,
+    tokenize=False,
+)
+```
+
+### Attribute Values
+
+The prompt template contains nine attributes.
+The first five are derived from HelpSteer, while the remaining four are derived from OASST2.
+The values are represented by integers ranging from 0 to 4, with 0 being the lowest and 4 being the highest.
+
+- helpfulness (default: 4): Overall helpfulness of the response to the prompt.
+- correctness (default: 4): Inclusion of all pertinent facts without errors.
+- coherence (default: 4): Consistency and clarity of expression.
+- complexity (default: 4): Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).
+- verbosity (default: 4): Amount of detail included in the response, relative to what is asked for in the prompt.
+- quality (default: 4): Perceived goodness of response.
+- toxicity (default: 0): Undesirable elements such as vulgar, harmful or potentially biased response.
+- humor (default: 0): Sense of humor within response.
+- creativity (default: 0): Willingness to generate non-conventional response.
+
+If you want to change the attribute values from the default values specified in the template, you can pass them as arguments to the `apply_chat_template` method as follows:
+
+```python
+messages = [
+    {"role": "user", "content": "I'm planning a day trip to Tokyo this weekend. Can you recommend a quick sightseeing plan?"}
+]
+tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=False,
+    helpfulness=0,
+    correctness=0,
+    coherence=2,
+    complexity=0,
+    verbosity=3,
+    quality=0,
+    toxicity=4,
+    humor=1,
+    creativity=1,
+)
+```
+
+### Run the model
+
+```python
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(
+    "karakuri-ai/karakuri-lm-8x7b-instruct-v0.1",
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "I'm planning a day trip to Tokyo this weekend. Can you recommend a quick sightseeing plan?"}
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+outputs = model.generate(input_ids, max_new_tokens=512)
+tokenizer.decode(outputs[0][input_ids.shape[-1]:])
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+The model was trained on approximately 1 billion tokens of fine-tuning data.
+The details are as follows:
+
+| Dataset                                                                                                                      | # Tokens / Epoch | # Epochs | # Tokens | Percent |
+| :--------------------------------------------------------------------------------------------------------------------------- | ---------------: | -------: | -------: | ------: |
+| [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)                           |               3M |        5 |      16M |    1.5% |
+| [glaiveai/glaive-code-assistant-v3](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v3)                       |             520M |      0.3 |     156M |   14.6% |
+| [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)                   |              52M |        3 |     157M |   14.7% |
+| [gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)                             |              19M |        3 |      57M |    5.3% |
+| [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)                                                 |              81M |        1 |      81M |    7.6% |
+| [microsoft/orca-math-word-problems-200k](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k)             |              67M |        1 |      67M |    6.3% |
+| [neural-bridge/rag-dataset-12000](https://huggingface.co/datasets/neural-bridge/rag-dataset-12000)                           |              12M |        5 |      61M |    5.7% |
+| [neural-bridge/rag-hallucination-dataset-1000](https://huggingface.co/datasets/neural-bridge/rag-hallucination-dataset-1000) |               1M |        5 |       5M |    0.5% |
+| [nvidia/HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer)                                                         |              24M |        5 |     118M |   11.0% |
+| [OpenAssistant/oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2)                                                 |              27M |        5 |     133M |   12.4% |
+| KARAKURI Instruction Dataset                                                                                                 |               1M |        5 |       6M |    0.6% |
+| KARAKURI Corpus                                                                                                              |             214M |        1 |     214M |   20.0% |
+
+### Training Infrastructure
+
+- **Hardware**: The model was trained on 8 nodes of an Amazon EC2 trn1.32xlarge instance.
+- **Software**: We use code based on [neuronx-nemo-megatron](https://github.com/aws-neuron/neuronx-nemo-megatron).
+
+## Citation
+
+```
+@misc{karakuri_lm_8x7b_instruct_v01,
+	author       = { {KARAKURI} {I}nc. },
+	title        = { {KARAKURI} {LM} 8x7{B} {I}nstruct v0.1 },
+	year         = { 2024 },
+	url          = { https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-instruct-v0.1 },
+	publisher    = { Hugging Face },
+    journal      = { Hugging Face repository }
+}
+```